3D medical image data is often acquired in the form of a set of slices of data. The set of slices defines volumetric, i.e. 3D, image data. This set may be referred to as an image data set or just as image data. The most common way of inspecting a large 3D medical data set from e.g. a CT scanner or similar device, is to load a data set from a storage medium and to display cross-sectional slices of that volume. The locations of interest in that volume are found by scrolling through the (often large number of) slices. Normally, this is the stack of original slices as they come from the acquisition device. This is a quite time consuming task, which includes navigating to the areas of interest and visually inspecting the slices. When inspecting the data like this, there is no help or assistance available to facilitate or accelerate finding of the target locations.
Thus, current procedure is to retrieve the complete data set, navigate through the acquired slices to locate an area or object of interest, inspect this area of interest, and navigate from one area of interest to the next by scrolling through the data when more then one object needs to be inspected.
Alternatively, when the user knows in which part of the data set the object of interest can be expected, a part of the data set can be retrieved. This requires however that the user inspects a number of thumbnail representations of slices at regular intervals in the data set, and decides which interval(s) to retrieve.
If multiple objects need to be inspected, the user will in general have to retrieve the whole data set or a part of it, navigate to a first area of interest, and locally scroll through slices that contain parts of this first object. After that the user will navigate to a next area of interest to inspect the next object, etc, until all objects have been inspected. Because there is no information available about the locations and sizes etc. of the objects relative to the data set, all of the data set has to be retrieved. Alternatively, a user may spend time on selecting a part of the data set that in any case covers a larger volume than the object(s) of interest.
All applications that require retrieving data from large data sets, for inspection of objects that are represented by parts of those data sets, take time. The number of slices that hold cross-sections with an object of interest can be much smaller that the total number of slices in a data set. Loading large data sets takes even more time, which is annoying for the user. If only a relatively small part of the data set is required, because the area of interest is much smaller that the complete covered volume, time and computer resources are wasted.
The problem of (nearly) useless data retrieval is becoming more and more pressing due to the increasing spatial resolution of current medical imaging modalities. Thus, previously maybe around 100-200 slices were obtained from an imaging session, now the number can typically be above 1000 slices, making manual analysis of such an amount of slices quite time consuming.
Current techniques for representing a large amount of data and giving a user an improved overview of the medical image data set can be found in for example US 2006/0085407, where a one-line list or thumbnails is used to give an overview of a large data set. However, the thumbnail pictures are chosen based on a numbering of the slices or pictures, which is not very effective for selecting relevant anatomical parts in the region of interest.
Hence, an improved method of retrieving data from a medical image data set would be advantageous, and in particular a more efficient and/or reliable method would be advantageous.